Add Claude Opus 4.6 entries for Anthropic, Bedrock Converse, and Vertex AI.
Align pricing and capability metadata with Anthropic docs, including
long-context rates, above-200k prompt-caching rates, prefill removal,
and tool-use system prompt token counts.
Register the Bedrock Converse model ID in constants and add targeted
tests to validate model map values and converse registration.
OpenAI's 400k context window is split between input and output:
- GPT-5/5.1/5.2 models: 272k input + 128k output = 400k context
- GPT-5-pro models: 128k input + 272k output = 400k context
Reference: https://openai.com/index/introducing-gpt-5-for-developers/
"In the API, all GPT-5 models can accept a maximum of 272,000 input
tokens and emit a maximum of 128,000 reasoning & output tokens"
Fixes incorrect 400k max_input_tokens values across 32 models.
- Add cerebras/zai-glm-4.7 with same specs as 4.6 (128K context, $2.25/M input, $2.75/M output)
- Mark cerebras/zai-glm-4.6 with deprecation_date: 2026-01-20
- Both models support function calling, reasoning, and tool choice
* fix: align max_tokens with max_output_tokens for consistency
Fixed inconsistent max_tokens definitions in model_prices_and_context_window.json.
According to LiteLLM convention, max_tokens should equal max_output_tokens when available.
Models fixed:
- deepseek-chat: 131072 → 8192 (now equals max_output_tokens)
- dashscope/qwen-flash: 1000000 → 32768 (now equals max_output_tokens)
- databricks/databricks-gemma-3-12b: 128000 → 32000 (now equals max_output_tokens)
This ensures consistency across all providers where max_tokens represents
the maximum number of tokens that can be generated in the output.
* fix: align max_tokens with max_output_tokens for 244 models
- Fix 244 models where max_tokens != max_output_tokens
- Add test to validate max_tokens consistency and prevent regressions
According to model_prices_and_context_window.json spec:
- max_tokens is a LEGACY parameter
- Should always equal max_output_tokens when both are present
This ensures consistency across all model definitions.
* fix: align max_tokens with max_output_tokens for consistency
Fixed inconsistent max_tokens definitions in model_prices_and_context_window.json.
According to LiteLLM convention, max_tokens should equal max_output_tokens when available.
Models fixed:
- deepseek-chat: 131072 → 8192 (now equals max_output_tokens)
- dashscope/qwen-flash: 1000000 → 32768 (now equals max_output_tokens)
- databricks/databricks-gemma-3-12b: 128000 → 32000 (now equals max_output_tokens)
This ensures consistency across all providers where max_tokens represents
the maximum number of tokens that can be generated in the output.
* fix(workflow): Update issue labeling with working regex pattern
- Replace contains() with regex pattern using \s* for flexible whitespace matching
- Consolidate 4 separate steps into single unified component labeling step
- Tested and verified pattern works for all components: SDK, Proxy, UI Dashboard, Docs
- Pattern handles GitHub's issue body formatting with ### headers and variable newlines
Add support for Z.AI GLM-4.7, latest flagship model with enhanced reasoning capabilities.
Changes:
- Add zai/glm-4.7 to model pricing with /bin/bash.60/M input, .20/M output
- Add cached input pricing (/bin/bash.11/M) for GLM-4.7
- Add supports_reasoning flag to enable thinking parameter
- Update ZAIChatConfig to support thinking parameter for models with reasoning
- Update documentation with GLM-4.7 as latest flagship model
- Add cached input column to pricing table (GLM-4.7 only)
- Add tests for GLM-4.7 reasoning support and cost calculation
- Update all examples to use GLM-4.7
Model specifications:
- Context: 200K input, 128K output
- Supports: reasoning, function calling, tool choice, prompt caching
- Pricing: Same as GLM-4.6 with cache support
See: https://docs.z.ai/guides/llm/glm-4.7
* fix(cost_calculator): correct gpt-image-1 cost calculation using token-based pricing (#13847)
gpt-image-1 uses token-based pricing (like chat models), not pixel-based pricing
like DALL-E. The old code was calculating incorrect costs by treating it as DALL-E.
Changes:
- Update model pricing JSON with correct token-based costs for gpt-image-1
- Add dedicated cost calculator for OpenAI gpt-image models
- Route gpt-image-1 to token-based calculator in cost router
- Add comprehensive tests for the new calculator
* refactor: simplify gpt-image-1 cost calculator using responses API helper
Reuse _transform_response_api_usage_to_chat_usage and generic_cost_per_token
for gpt-image-1 cost calculation since ImageUsage has the same spec as
ResponseAPIUsage.
